Scilab 6.1.0

Scilab Help >> Signal Processing > Convolution - Correlation > conv2

# conv2

discrete 2-D convolution.

### Syntax

C = conv2(A,B [,shape]) C = conv2(hrow,hcol,B [,shape])

### Parameters

- hrow
a real or complex vector.

- hcol
a real or complex vector.

- A
a real or complex 2-D array.

- B
a real or complex 2-D array.

- shape
an optional character string with possible values:

`"full"`

,`conv2`

computes the full two-dimensional convolution. It is the default value.`"same"`

,`conv2`

computes the central part of the convolution of the same size as`A`

.`"valid"`

,`conv2`

computes the convolution parts without the zero-padding of`A`

.

- C
a real or complex 2-D array.

### Description

`conv2`

uses a straightforward formal
implementation of the two-dimensional convolution equation in
spatial form.

`C=conv2(A,B [,shape])`

computes the
two-dimensional convolution of the arrays `A`

and `B`

:

- With
`shape=="full"`

the dimensions of the result`C`

are given by`size(A)+size(B)+1`

. The indices of the center element of`B`

are defined as`floor((size(B)+1)/2)`

. - With
`shape=="same"`

the dimensions of the result`C`

are given by`size(A)`

. The indices of the center element of`B`

are defined as`floor((size(B)+1)/2)`

. - With
`shape=="valid"`

the dimensions of the result`C`

are given by`size(A)-size(B)+1)`

if`and(size(A)-size(B))>=0`

else`C`

is empty . The indices of the center element of`B`

are defined as`[1 1]`

.

The separable form `C=conv2(hrow,hcol,B [,shape])`

is equivalent to `C=conv2(hrow(:)*hcol(:).',B [,shape])`

Note that convol2d can be more efficient for large arrays.

### Examples

s = [1 2 1; 0 0 0; -1 -2 -1];//Sobel horizontal edge kernel A = zeros(10,10);A(3:7,3:7) = 1; conv2(s,A); //separable form u=[1;0;-1];v=[1 2 1];// u*v=s conv2(u,v,A)

### History

Version | Description |

5.4.0 | Function conv2 introduced. |

## Comments

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